matplotlib-publication-figures

name	matplotlib-publication-figures
description	Academic figure standards using matplotlib, color accessibility, DPI settings, LaTeX integration. Use when creating charts, plots, and figures for publication.

Matplotlib Publication Figures

Create publication-ready, high-quality figures using matplotlib. Follow these guidelines.

Sizing and Layout

• Determine the target column width of the academic template (e.g., ACM or IEEE two-column is usually ~3.3 to 3.5 inches per column).
• Set the figure size explicitly in inches to avoid scaling artifacts when inserted into the paper.

  fig, ax = plt.subplots(figsize=(3.5, 2.5)) # Single column
  fig, ax = plt.subplots(figsize=(7.0, 3.0)) # Double column (span)
  

• Use fig.tight_layout() or constrained_layout=True to minimize whitespace and ensure labels aren't clipped.

Typography and LaTeX Integration

• Fonts in the figure should ideally match the paper's font style and size (e.g., size 9 or 10).
• Avoid fonts that are too small to read when printed. Minimum font size is usually 8pt.
• Enable LaTeX text rendering if equations or specific fonts are needed:

  plt.rcParams.update({
      "text.usetex": True,
      "font.family": "serif",
      "font.serif": ["Times", "Palatino", "Computer Modern Roman"]
  })
  

Colors and Accessibility

• Never rely on color alone to distinguish data. Use varying line styles (-, --, -., :) or markers (o, s, ^, x).
• Use colorblind-friendly palettes. Consider palettes like viridis, plasma, cividis, or specific categorical palettes designed for accessibility (e.g., seaborn's colorblind palette).
• Ensure high contrast between text/lines and the background.

Styling Elements

• Include a clear, descriptive legend. For complex charts, place the legend outside the plot box to avoid overlapping data:

  ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=3)
  

• Label all axes with both the metric and its unit (e.g., Throughput (Tx/sec)).
• Turn on appropriate gridlines (ax.grid(True, linestyle='--', alpha=0.6)) to help the reader estimate values, but ensure they don't overpower the data series.
• Remove top and right spines if they are not necessary to reduce chart junk:

  ax.spines['top'].set_visible(False)
  ax.spines['right'].set_visible(False)
  

Exporting for Publication

• Export vector graphics for plots (PDF or EPS) so they scale infinitely without pixelation.
• If raster images must be used (e.g., for massive scatter scale or heatmaps), use a high DPI:

  plt.savefig("figure.pdf", format="pdf", bbox_inches="tight")
  plt.savefig("retina_figure.png", dpi=300, bbox_inches="tight")